Global Income Inequality and Savings: A Data Science Perspective

A society or country with income equally distributed among its people is truly a fiction! The phenomena of socioeconomic inequalities have been plaguing mankind from times immemorial. We are interested in gaining an insight about the co-evolution of the countries in the inequality space, from a data science perspective. For this purpose, we use the time series data for Gini indices of different countries, and construct the equal-time cross-correlation matrix. We then use this to construct a similarity matrix and generate a map with the countries as different points generated through a multi-dimensional scaling technique. We also produce a similar map of different countries using the time series data for Gross Domestic Savings (% of GDP). We also pose a different, yet significant, question: Can higher savings moderate the income inequality? In this paper, we have tried to address this question through another data science technique - linear regression, to seek an empirical linkage between the income inequality and savings, mainly for relatively small or closed economies. This question was inspired from an existing theoretical model proposed by Chakraborti-Chakrabarti (2000), based on the principle of kinetic theory of gases. We tested our model empirically using Gini index and Gross Domestic Savings, and observed that the model holds reasonably true for many economies of the world.Comment: 8 pages, 6 figures. IEEE format. Accepted for publication in 5th IEEE DSAA 2018 conference at Torino, Ital

Identifying Early Warning Signals from News Using Network Community Detection

The paper addresses the challenge of accelerating identification of changes in risk drivers in the insurance industry. Specifically, the work presents a method to identify significant news events ("signals") from batches of news data to inform Life & Health insurance decisions. Signals are defined as events that are relevant to a tracked risk driver, widely discussed in multiple news outlets, contain novel information and affect stakeholders. The method converts unstructured data (news articles) into a sequence of keywords by employing a linguistic knowledge graph-based model. Then, for each time window, the method forms a graph with extracted keywords as nodes and draws weighted edges based on keyword co-occurrences in articles. Lastly, events are derived in an unsupervised way as graph communities and scored for the requirements of a signal: relevance, novelty and virality. The methodology is illustrated for a Life & Health topic using news articles from Dow Jones DNA proprietary data set, and assessed against baselines on a publicly available news data set. The method is implemented as an analytics engine in Early Warning System deployed at Swiss Re for the last 1.5 years to extract relevant events from live news data. We present the system's architectural design in production and discuss its use and impact

Internal Polymerization of Epoxy Group of Epoxidized Natural Rubber by Ferric Chloride and Formation of Strong Network Structure

In this work, studies are carried out to understand the crosslinking reaction of epoxidized natural rubber (50 mol% epoxy, ENR-50) by metal ion namely ferric ion (Fe3+, FeCl3, ferric chloride). It is found that a small amount of FeCl3 can cure ENR to a considerable extent. A direct interaction of the ferric ion with the epoxy group as well as internal polymerization enable the ENR to be cured in an efficient manner. It was also found that with the increased concentration of FeCl3, the crosslinking density of the matrix increased and therefore, the ENR offers higher mechanical properties (i.e., modulus and tensile strength). In addition, the glass transition temperature (tg) of ENR vulcanizate is increased with increasing concentration of FeCl3. Moreover, the thermal degradation temperature (Td) of the ENR-FeCl3 compound was shifted toward higher temperature as increasing concentration FeCl3